{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import  classification_report\n",
    "from sklearn import preprocessing\n",
    "from sklearn import linear_model\n",
    "# 数据是否需要标准化\n",
    "scale = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 加载数据\n",
    "data = np.genfromtxt('LR-testSet.csv',delimiter = ',')\n",
    "x_data = data[:,:-1]\n",
    "y_data = data[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   -0.017612  14.053064  0\n",
       "0  -1.395634   4.662541  1\n",
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('LR-testSet.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "       [ 7.8557000e-02,  5.9736000e-02],\n",
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       "       [ 3.1702900e-01,  1.4739025e+01]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., 0., 0., 1., 0., 1., 0., 0., 1., 0., 1., 0., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 0., 1., 1., 0., 0., 1., 1., 0., 1., 1., 0., 1.,\n",
       "       1., 0., 0., 0., 0., 0., 1., 1., 0., 1., 1., 0., 1., 1., 0., 0., 0.,\n",
       "       0., 0., 0., 1., 1., 0., 1., 0., 1., 1., 1., 0., 0., 0., 1., 1., 0.,\n",
       "       0., 0., 0., 1., 0., 1., 0., 0., 1., 1., 1., 1., 0., 1., 0., 1., 1.,\n",
       "       1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 1., 0., 0.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot():\n",
    "    x0 = []\n",
    "    x1 = []\n",
    "    y0 = []\n",
    "    y1 = []\n",
    "    # 切分不同类别的数据\n",
    "    for i in range(len(x_data)):\n",
    "        if y_data[i] == 0:\n",
    "            x0.append(x_data[i,0])\n",
    "            y0.append(x_data[i,1])\n",
    "        else:\n",
    "            x1.append(x_data[i,0])\n",
    "            y1.append(x_data[i,1])\n",
    "    # 画图\n",
    "    scatter0 = plt.scatter(x0,y0,c = 'b',marker = 'o')\n",
    "    scatter1 = plt.scatter(x1,y1,c = 'r',marker = 'x')\n",
    "    #画图例\n",
    "    plt.legend(handles=[scatter0,scatter1],labels=['label0','label1'],loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic = linear_model.LogisticRegression()\n",
    "logistic.fit(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if scale == False:\n",
    "    # 画图决策边界\n",
    "    plot()\n",
    "    x_test = np.array([[-4],[3]])\n",
    "    y_test = (-logistic.intercept_ - x_test*logistic.coef_[0][0])/logistic.coef_[0]\n",
    "    [1]\n",
    "    plt.plot(x_test, y_test, 'k')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
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